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Machine Learning in R
- Location
- Start Date
- Cost
- : Zoom Sessions
- : February 07, 2026
- : $350
Learning Objectives
By the end of the course, students will be able to:
- Apply machine learning workflows in R for real-world datasets
- Build, tune, and evaluate predictive and unsupervised models.
- Implement feature engineering and interpret model outputs.
- Communicate findings using reproducible R Markdown or Shiny dashboards.
- Design and present an end-to-end ML project.
Using R for Data management and Reporting
- Location
- Start Date
- Cost
- : Zoom Sessions
- : February 07, 2026
- : $350
Learning Objectives
By the end of the course, students will be able to:
- Import, clean, and manage data from multiple file formats in R
- Apply tidy data principles to restructure datasets for analysis
- Handle missing, inconsistent, and duplicate data effectively
- Create reproducible and well-documented data management workflows
- Validate and prepare datasets for analysis, reporting, or sharing
- Produce elegant figures and tables summarizing a dataset.
- Produce a polished report using RMarkdown or R Notebooks.
- Write custom functions in R
- Basic modeling through R
Machine Learning for Survival Analysis in R
- Location
- Start Date
- Cost
- : Zoom Sessions
- : March 14, 2026
- : $350
Learning Objectives
By the end of the course, students will be able to:
- Understand and apply core survival analysis concepts such as censoring, hazards, and time-to-event risk
- Build and interpret Kaplan–Meier and Cox proportional hazards models in R
- Apply machine learning methods for survival data, including penalized Cox models, Random Survival Forests, boosting, and deep learning approaches
- Evaluate and compare survival models using C-index, Brier score, calibration, and time-dependent AUC
- Use tidymodels and modern R workflows to create reproducible survival ML pipelines
- Interpret and explain model results using variable importance and explainability tools
- Communicate survival ML insights clearly to public health, clinical, and policy audiences
Machine Learning in R
Step Into the Future - Master Machine Learning, AI & R!
Transform your data skills through real projects, interactive labs, and expert mentorship.
Enroll now with oores Analytics LLC and power your path into AI and data science.
About the training
This 10-days (5 weekends) Machine Learning in R program (oores Analytics LLC edition) covers the complete lifecycle: Core ML foundations (supervised learning, regression), Advanced models & unsupervised learning, Model tuning and explainability, Deployment, integration, and capstone project
Who should register?
It is expected that participants in this course will be advanced undergraduate, graduate students, physicians, faculty members, or applied researchers who seek to know more about the potential to apply Machine Learning in their own work. Basic familiarity with R programming and statistics is assumed, but no prior experience ML experience is required.
The presentation will be conceptually rather than mathematically oriented. The overall goal of the course is to help you implement Machine Learning models through the use of best analysis and reporting practices.
Machine Learning in R
- Location
- Start Date
- Cost
- : Zoom Sessions
- : February 07, 2026
- : $350
Learning Objectives
By the end of the course, students will be able to:
- Apply machine learning workflows in R for real-world datasets
- Build, tune, and evaluate predictive and unsupervised models.
- Implement feature engineering and interpret model outputs.
- Communicate findings using reproducible R Markdown or Shiny dashboards.
- Design and present an end-to-end ML project.
Contact Us
- info@ooresanalytics.com
- +1-(765)-315-1382